Domain adaptation aims at helping a trained model better generalize to unseen test data. Adversarial training [1]}, [2]}, [3]}, [4]}, [5]}, [6]} is a typical structure for UDA for semantic segmentation, which aims to address domain discrepancy between source and target domains by aligning feature distributions via an adversarial network. UDA via an adversarial network consists of two sub-networks. A discriminator aims to distinguish between domain the input feature maps, while a generator aims at generating feature maps to fool the discriminator. In this way, the discriminator is able to minimize the discrepancy of feature representations in the two domains. A similar approach for domain adaptation for semantic segmentation uses generative networks by conditioning target images on the source domain [7]}, [8]}, [9]}.
Another category of approaches addressing UDA involves self-training [10]}, [11]}, [9]}, [13]}. The core idea in such approaches is to generate pseudo labels for unlabeled target domain data using an ensemble of previous models.
While, recent UDA methods for semantic segmentation have achieved significant progress, there is still a large performance gap in comparison to their fully supervised counterparts without target supervision.
